Gastos Casa
26 August, 2024
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 13/7/2024 | Comida | 28083 | Tami | Supermercado |
| 13/7/2024 | Comida | 32210 | Andrés | comida maite e ida farmacia |
| 15/7/2024 | Comida | 11250 | Tami | Minimarket Maite |
| 16/7/2024 | Comida | 4900 | Tami | Torta Maite |
| 16/7/2024 | Comida | 65645 | Andrés | gastos maite (no va lo de mercado pago, q taché en negrita) |
| 18/7/2024 | Comida | 36687 | Tami | Supermercado |
| 18/7/2024 | Comida | 4772 | Andrés | NA |
| 20/7/2024 | VTR | 21990 | Andrés | NA |
| 12/7/2024 | Uber | 2537 | Tami | Vuelta Uber casa delox |
| 25/7/2024 | Comida | 36175 | Andrés | lider pickup |
| 26/7/2024 | Gas | 72850 | Andrés | NA |
| 27/7/2024 | Comida | 4300 | Andrés | biblioteca san camilo |
| 27/7/2024 | Comida | 27300 | Andrés | NA |
| 28/7/2024 | Comida | 67250 | Tami | Supermercado |
| 30/7/2024 | Comida | 30650 | Andrés | piwén |
| 1/8/2024 | Electricidad | 209425 | Andrés | NA |
| 3/8/2024 | Comida | 45130 | Tami | Supermercado |
| 3/8/2024 | Comida | 19490 | Tami | Barritas Wild Soul |
| 11/8/2024 | Comida | 8230 | Tami | Supermercado |
| 8/8/2024 | Comida | 5000 | Andrés | platano y cosas |
| 15/8/2024 | Electricidad | 190084 | Andrés | enel rqlo (dupliqué el gasto para que pagues eso. Yo ya ni uso mi estufa, y tal vez tú deberías usar la mía porque tiene temporizador) |
| 18/8/2024 | VTR | 21990 | Andrés | NA |
| 16/8/2024 | Comida | 11756 | Andrés | lider |
| 19/8/2024 | Comida | 91135 | Tami | Supermercado |
| 23/8/2024 | Otros | 170000 | Andrés | instalacion |
| 23/8/2024 | Otros | 285000 | Andrés | aire |
| 25/8/2024 | Gas | 75000 | Andrés | NA |
| 25/8/2024 | Comida | 66781 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 8.0615e+08 2 6.5386 0.0015 **
## lag_depvar 1.2982e+11 1 2105.9806 <2e-16 ***
## Residuals 4.6234e+10 750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 396.1268 14061.55 0.0351673
## 2-0 29922.412 23751.9819 36092.84 0.0000000
## 2-1 22693.574 19096.9614 26290.19 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 247 34197.57 2 30013.29
## 248 37430.14 2 34197.57
## 249 26932.43 2 37430.14
## 250 33729.86 2 26932.43
## 251 38081.43 2 33729.86
## 252 44028.00 2 38081.43
## 253 47139.71 2 44028.00
## 254 46558.86 2 47139.71
## 255 58350.57 2 46558.86
## 256 78380.00 2 58350.57
## 257 78168.29 2 78380.00
## 258 70510.86 2 78168.29
## 259 72207.14 2 70510.86
## 260 67881.00 2 72207.14
## 261 69536.43 2 67881.00
## 262 62390.71 2 69536.43
## 263 50113.14 2 62390.71
## 264 45565.57 2 50113.14
## 265 45805.29 2 45565.57
## 266 41348.57 2 45805.29
## 267 51426.86 2 41348.57
## 268 47160.57 2 51426.86
## 269 51907.43 2 47160.57
## 270 49751.43 2 51907.43
## 271 54407.43 2 49751.43
## 272 54746.29 2 54407.43
## 273 61634.57 2 54746.29
## 274 58926.43 2 61634.57
## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
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## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
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## 327 63652.57 2 72865.71
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## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
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## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
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## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
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## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
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## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
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## 358 40957.29 2 38509.29
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## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
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## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
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## 396 60012.57 2 60603.43
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## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
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## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
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## 431 39328.29 2 39456.29
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## 433 30875.43 2 35296.14
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## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
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## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
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## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
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## 455 35547.57 2 47139.43
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## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
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## 463 50720.71 2 50490.00
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## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
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## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
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## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
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## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
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## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
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## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
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## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
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## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
## 576 59576.00 2 71741.00
## 577 52390.29 2 59576.00
## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
## 586 60895.57 2 52488.14
## 587 59856.57 2 60895.57
## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
## 590 52190.57 2 51874.57
## 591 41562.43 2 52190.57
## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
## 594 43473.14 2 38612.71
## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
## 600 57391.71 2 61789.71
## 601 62902.29 2 57391.71
## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
## 608 52399.57 2 59065.00
## 609 60483.43 2 52399.57
## 610 58262.71 2 60483.43
## 611 54939.71 2 58262.71
## 612 51169.00 2 54939.71
## 613 43113.29 2 51169.00
## 614 56289.71 2 43113.29
## 615 60739.86 2 56289.71
## 616 50363.14 2 60739.86
## 617 62270.86 2 50363.14
## 618 67061.57 2 62270.86
## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
## 621 68023.29 2 85054.00
## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
## 631 55706.29 2 57944.71
## 632 88593.71 2 55706.29
## 633 77359.43 2 88593.71
## 634 79878.71 2 77359.43
## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
## 637 67381.43 2 75716.00
## 638 63528.57 2 67381.43
## 639 49682.86 2 63528.57
## 640 47815.00 2 49682.86
## 641 46546.14 2 47815.00
## 642 44808.71 2 46546.14
## 643 42959.57 2 44808.71
## 644 46023.86 2 42959.57
## 645 51309.57 2 46023.86
## 646 68447.29 2 51309.57
## 647 84959.29 2 68447.29
## 648 81666.29 2 84959.29
## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
## 651 104812.71 2 89422.14
## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
## 660 52126.71 2 47184.00
## 661 51202.00 2 52126.71
## 662 64437.14 2 51202.00
## 663 64297.14 2 64437.14
## 664 64628.57 2 64297.14
## 665 51413.14 2 64628.57
## 666 52969.43 2 51413.14
## 667 54135.29 2 52969.43
## 668 48799.43 2 54135.29
## 669 41907.86 2 48799.43
## 670 45382.00 2 41907.86
## 671 42633.29 2 45382.00
## 672 46624.71 2 42633.29
## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
## 675 29737.71 2 35852.86
## 676 29734.86 2 29737.71
## 677 32881.71 2 29734.86
## 678 38298.57 2 32881.71
## 679 40886.14 2 38298.57
## 680 38601.86 2 40886.14
## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
## 696 40300.14 2 47513.00
## 697 33312.43 2 40300.14
## 698 29556.71 2 33312.43
## 699 27816.71 2 29556.71
## 700 34120.29 2 27816.71
## 701 32132.57 2 34120.29
## 702 32902.57 2 32132.57
## 703 39694.14 2 32902.57
## 704 72501.29 2 39694.14
## 705 79551.14 2 72501.29
## 706 99637.71 2 79551.14
## 707 95424.29 2 99637.71
## 708 98395.14 2 95424.29
## 709 115594.71 2 98395.14
## 710 114267.57 2 115594.71
## 711 88353.29 2 114267.57
## 712 88750.86 2 88353.29
## 713 78835.71 2 88750.86
## 714 75519.14 2 78835.71
## 715 73202.86 2 75519.14
## 716 53433.29 2 73202.86
## 717 48165.71 2 53433.29
## 718 52163.14 2 48165.71
## 719 49306.86 2 52163.14
## 720 36846.86 2 49306.86
## 721 43220.57 2 36846.86
## 722 38952.29 2 43220.57
## 723 41522.29 2 38952.29
## 724 39090.00 2 41522.29
## 725 28452.57 2 39090.00
## 726 32975.00 2 28452.57
## 727 33690.71 2 32975.00
## 728 26405.29 2 33690.71
## 729 47087.43 2 26405.29
## 730 49660.29 2 47087.43
## 731 47409.71 2 49660.29
## 732 53881.71 2 47409.71
## 733 45189.57 2 53881.71
## 734 45503.86 2 45189.57
## 735 54640.14 2 45503.86
## 736 39131.29 2 54640.14
## 737 35024.14 2 39131.29
## 738 44755.43 2 35024.14
## 739 41063.29 2 44755.43
## 740 42783.29 2 41063.29
## 741 45952.57 2 42783.29
## 742 44937.43 2 45952.57
## 743 40838.43 2 44937.43
## 744 48838.43 2 40838.43
## 745 43139.14 2 48838.43
## 746 67134.29 2 43139.14
## 747 73224.29 2 67134.29
## 748 68770.71 2 73224.29
## 749 59539.29 2 68770.71
## 750 82179.86 2 59539.29
## 751 74252.14 2 82179.86
## 752 73015.00 2 74252.14
## 753 56116.43 2 73015.00
## 754 111885.00 2 56116.43
## 755 131425.14 2 111885.00
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 598 52156.67 16605.481
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2023.294877 4042.261135 -539.912194 2436.476171 -2973.270889
## 7 8 9 10 11
## 517.423319 -5657.793148 -1185.501804 -3963.588222 -413.015013
## 12 13 14 15 16
## -4935.457275 -1602.180419 -892.553024 384.102917 -3237.743400
## 17 18 19 20 21
## -371.229799 -2124.338113 6610.485347 -1529.407705 -1207.698844
## 22 23 24 25 26
## 1476.666702 -1187.281623 234.576589 1694.352738 -7104.345166
## 27 28 29 30 31
## 950.876565 8194.274098 413.126271 -19.029479 -2405.248232
## 32 33 34 35 36
## 1573.742011 4568.832839 1119.836134 2384.116905 -1876.539600
## 37 38 39 40 41
## 4601.689333 4300.078175 -2282.026999 -2985.156539 -1111.043856
## 42 43 44 45 46
## -10741.374930 7297.896258 2558.890180 1366.211732 8103.500004
## 47 48 49 50 51
## 678.260235 6521.310180 6702.709140 -5897.864060 -4804.343659
## 52 53 54 55 56
## -5063.707860 -7928.035117 6136.554880 -4075.616123 -4890.255290
## 57 58 59 60 61
## 3863.207798 891.184858 -29.941587 144.104547 -4994.946732
## 62 63 64 65 66
## 18132.008185 3630.807489 -3657.605491 5918.244908 7333.177956
## 67 68 69 70 71
## 14623.659383 1668.417772 -13235.582702 -1315.441598 4636.535457
## 72 73 74 75 76
## -4910.002140 -4409.008257 -10497.701207 2475.351174 -5394.131618
## 77 78 79 80 81
## 1073.255698 -6858.704311 559.293097 -2344.087727 -2682.643884
## 82 83 84 85 86
## -3919.725158 -523.525227 2327.391254 3771.962251 481.853730
## 87 88 89 90 91
## -480.683032 200.326847 4304.963820 -1163.992678 1150.919431
## 92 93 94 95 96
## -2065.399291 -1043.415424 179.196420 276.020710 -7483.211215
## 97 98 99 100 101
## 2398.800186 -8598.292118 -2929.479363 -4027.039270 -1722.171932
## 102 103 104 105 106
## -1246.874711 3194.893411 -2332.479090 2604.443859 -1151.469953
## 107 108 109 110 111
## 977.536199 2592.467495 -3151.893826 -4718.169833 -841.916858
## 112 113 114 115 116
## 1911.602873 11699.025317 -1247.763594 2665.074673 4257.587695
## 117 118 119 120 121
## 3494.524486 -1109.969186 -4724.080579 -3726.773374 2320.689195
## 122 123 124 125 126
## -1733.736384 1341.029218 8857.711186 839.183595 122.881361
## 127 128 129 130 131
## -2527.918669 2651.464859 7047.187747 1001.860039 -8509.450788
## 132 133 134 135 136
## 1747.673832 4132.728033 -3169.866546 -1422.180134 -854.872051
## 137 138 139 140 141
## -3880.019937 1186.385131 -493.519299 -2911.431913 1722.580855
## 142 143 144 145 146
## -1878.653556 -7825.555044 2049.430424 -3472.727184 2111.287583
## 147 148 149 150 151
## -251.391167 1028.518998 -355.422926 1355.845571 1188.625316
## 152 153 154 155 156
## 3357.265172 -4864.088223 -1172.345488 -3232.958650 5961.966538
## 157 158 159 160 161
## 9746.120944 -3577.578727 -4922.168218 3463.020926 48.936604
## 162 163 164 165 166
## 2548.904852 -6061.528793 -6893.823452 4015.247629 17244.841454
## 167 168 169 170 171
## 3454.704829 -575.598147 -2621.440158 -1275.625169 3420.830513
## 172 173 174 175 176
## -402.242622 -8248.557471 2701.034486 4158.312787 452.472747
## 177 178 179 180 181
## 8576.808501 -9433.155511 -3642.950565 -10911.433021 -11393.838949
## 182 183 184 185 186
## 1093.312193 9146.415102 -1592.300076 5766.645321 6382.930086
## 187 188 189 190 191
## 12974.400463 8225.472104 -4281.794003 2252.763831 10152.661823
## 192 193 194 195 196
## -1875.700355 -2671.929540 -10502.058817 -6566.423410 1041.267201
## 197 198 199 200 201
## -5427.651184 -9981.466295 5214.005430 -3247.844418 -1887.474611
## 202 203 204 205 206
## -977.421481 6320.928569 9694.207027 369.975511 2715.257475
## 207 208 209 210 211
## 2883.743750 5565.756280 12606.453313 -5934.799697 -11528.094149
## 212 213 214 215 216
## -5873.947666 -10782.795863 -5250.556071 1359.775465 -13181.884960
## 217 218 219 220 221
## 16240.289884 7626.043961 1328.620206 26485.745049 12271.845219
## 222 223 224 225 226
## 7060.704707 13744.997613 -4214.461908 -2024.311525 3506.116539
## 227 228 229 230 231
## 89.843919 2484.160787 8746.275323 5565.114586 -2170.702559
## 232 233 234 235 236
## -2079.137224 9185.700134 -11759.508503 -7506.793490 -8747.731268
## 237 238 239 240 241
## -10290.845887 2906.530284 1173.725213 -8478.815561 -9156.550726
## 242 243 244 245 246
## 8942.268988 -7938.625356 2326.883199 -10469.398282 -4205.138918
## 247 248 249 250 251
## 1274.884559 847.652799 -12477.443751 3501.854997 1908.026791
## 252 253 254 255 256
## 4048.477874 1958.998321 -1343.532876 10956.230565 20671.986389
## 257 258 259 260 261
## 2941.447732 -4530.804044 3863.083941 -1946.722387 3492.585322
## 262 263 264 265 266
## -5101.056562 -11128.598783 -4937.540460 -720.273600 -5386.654999
## 267 268 269 270 271
## 8589.714705 -4491.586213 3986.795765 -2321.062902 4220.691612
## 272 273 274 275 276
## 487.158673 7079.061554 -1653.949522 11787.596219 -4851.753661
## 277 278 279 280 281
## 1471.395259 -628.753750 7598.217789 -5328.616244 -2984.776377
## 282 283 284 285 286
## -11504.100735 -2878.771791 18452.611475 7529.485807 2461.263982
## 287 288 289 290 291
## -904.841807 636.046754 6129.810520 6600.037016 -19068.754542
## 292 293 294 295 296
## -11372.204953 -8317.711991 9493.810348 2870.645701 -1389.481851
## 297 298 299 300 301
## 27195.810294 9775.369697 4586.883691 9198.397960 2518.080782
## 302 303 304 305 306
## -1367.257250 7577.222078 -24627.905699 -3775.591130 -398.642466
## 307 308 309 310 311
## -7186.590645 -4163.477166 2755.400431 -9378.137592 -3383.942694
## 312 313 314 315 316
## -8330.019938 1447.488144 -3278.906727 1926.769234 -4216.749921
## 317 318 319 320 321
## 27319.755854 -969.948195 3050.143731 10580.250659 5305.023598
## 322 323 324 325 326
## 32084.432831 4713.876063 -21329.766086 1494.024219 817.244815
## 327 328 329 330 331
## -6751.173243 -1987.144610 -33506.926541 803.464199 -2385.558339
## 332 333 334 335 336
## -169.920080 -3247.323235 4014.380140 -528.551453 -7045.786423
## 337 338 339 340 341
## -3186.703239 -2255.139187 -7740.535479 3813.647388 -1433.424521
## 342 343 344 345 346
## -1801.804788 -1058.006061 109.519173 407.065366 -1701.499165
## 347 348 349 350 351
## -9529.418892 -13262.640121 2299.662138 -4355.579469 -3684.441022
## 352 353 354 355 356
## -6002.549717 1740.149675 1353.090969 2703.791286 -3838.584771
## 357 358 359 360 361
## -582.587322 603.536529 6928.097267 153.810193 -166.614728
## 362 363 364 365 366
## 2451.179382 -2895.952781 -1012.056046 -8875.329479 -4720.240687
## 367 368 369 370 371
## -6289.903843 -5004.899235 -7293.635629 4996.411092 317.295824
## 372 373 374 375 376
## 7054.451056 -7742.052654 -2338.920974 -3459.818370 -2530.129484
## 377 378 379 380 381
## -12516.600138 1890.999342 -10667.073467 5699.194065 9298.304632
## 382 383 384 385 386
## 3033.716156 -2512.818976 1495.997579 6622.357369 11252.890897
## 387 388 389 390 391
## -6016.033077 -5551.291654 -323.052712 8396.447044 1606.635939
## 392 393 394 395 396
## 11006.164535 -10144.957737 2556.944269 484.493058 334.086794
## 397 398 399 400 401
## -881.260365 -783.948579 -14702.224500 8384.605608 -1356.281655
## 402 403 404 405 406
## -1538.855889 6824.234914 -8122.374121 -1443.897460 -2669.741555
## 407 408 409 410 411
## -5942.850336 -2953.132846 -3998.774258 -8821.022782 6104.960927
## 412 413 414 415 416
## 1572.077228 -7457.014433 -7745.355444 14197.414612 3708.104864
## 417 418 419 420 421
## 4356.519615 -8198.942908 -4867.423863 -2701.718506 2729.721687
## 422 423 424 425 426
## -14118.930098 -2833.139203 -9134.424964 3013.320327 6949.568690
## 427 428 429 430 431
## 6501.240039 -4102.638957 -4219.633590 -4804.543904 -1853.761024
## 432 433 434 435 436
## -5773.948140 -6667.931630 -5967.763764 -1393.947914 -854.931184
## 437 438 439 440 441
## -4990.984848 2577.689594 4809.240794 -5122.159979 -2206.891941
## 442 443 444 445 446
## 1529.205967 -3901.103982 2782.702105 -6653.254416 -12160.153721
## 447 448 449 450 451
## -4511.685615 9653.639925 -2082.561015 4705.741498 -5948.485836
## 452 453 454 455 456
## -1178.677503 326.353762 2960.316648 -12354.568689 3335.726735
## 457 458 459 460 461
## -6759.282304 6488.406045 2939.241451 2414.634816 -3953.327866
## 462 463 464 465 466
## 2001.091847 -112.017296 1686.187479 -638.263833 3234.787298
## 467 468 469 470 471
## -2771.368876 5686.394982 -7087.355563 -3073.523545 -2299.915662
## 472 473 474 475 476
## -4748.653891 2931.384896 7714.371718 -6138.674000 1391.477113
## 477 478 479 480 481
## -6279.687966 -2917.465032 1948.786170 -13006.413869 -9777.568425
## 482 483 484 485 486
## -1190.742017 24.640794 -969.827471 -1356.023396 -9603.141070
## 487 488 489 490 491
## 11108.913134 6186.944523 7337.366606 -5556.090414 5272.173323
## 492 493 494 495 496
## 9171.836434 5891.518855 -13658.561726 -10683.430585 -3510.035237
## 497 498 499 500 501
## -1162.442029 -580.349808 -7684.108065 582.620280 4249.712728
## 502 503 504 505 506
## 5445.596986 569.024562 -14.998094 -7335.704329 504.552650
## 507 508 509 510 511
## -5119.595506 1780.477318 -1361.271252 -8220.671441 -633.426837
## 512 513 514 515 516
## -2710.264825 -618.627047 1296.596317 -9543.430088 -7778.407096
## 517 518 519 520 521
## 24297.063585 9718.680737 5729.268187 -5507.982270 2654.950493
## 522 523 524 525 526
## 16866.444996 11250.054843 -24412.397806 -5202.902716 -3850.594054
## 527 528 529 530 531
## 4472.347444 -477.437055 -11220.846752 4322.838982 13818.136353
## 532 533 534 535 536
## -5131.155737 4244.621857 5407.734622 -1959.329734 -4696.835706
## 537 538 539 540 541
## -7205.463422 -2198.015569 8234.113100 1.040603 -8266.201046
## 542 543 544 545 546
## 1728.925387 -695.997995 271.844108 -11127.738393 -11110.975554
## 547 548 549 550 551
## 2034.221328 6978.529252 -1380.936499 775.215276 -7790.288051
## 552 553 554 555 556
## 8525.447981 823.462042 -12033.902077 9122.337900 8570.257384
## 557 558 559 560 561
## -29.630266 4724.814785 -3723.082554 13978.259837 21307.379310
## 562 563 564 565 566
## -6746.211231 -9916.651385 6593.446710 21.428117 3257.864265
## 567 568 569 570 571
## -7580.719599 -17468.788596 6585.909500 6328.308733 1769.646156
## 572 573 574 575 576
## 2961.364332 1623.900174 -2313.540136 14583.268849 -9844.008567
## 577 578 579 580 581
## -6389.554177 8597.461200 2707.934399 -6703.684565 7384.796141
## 582 583 584 585 586
## -3954.009099 -2907.795905 15585.980676 -14682.255037 8315.155750
## 587 588 589 590 591
## -77.437191 -6355.242877 -864.906368 146.818594 -10757.714999
## 592 593 594 595 596
## 1739.949366 -7211.872141 3028.929435 8809.592181 -7598.956934
## 597 598 599 600 601
## 5785.421245 2640.990392 6749.895871 -3324.360164 6032.940557
## 602 603 604 605 606
## -8438.761181 2155.419328 1161.752766 3026.720773 1372.777260
## 607 608 609 610 611
## 272.290197 -5933.320152 7980.482268 -1310.811841 -2691.454704
## 612 613 614 615 616
## -3555.692992 -8313.336088 11909.057038 4834.380944 -9434.669604
## 617 618 619 620 621
## 11549.081698 5924.663345 -5718.126585 26245.296519 -13040.994581
## 622 623 624 625 626
## -6926.008336 3047.187577 -4277.555330 -10688.591022 11245.494211
## 627 628 629 630 631
## -21733.535411 -2425.356669 8667.873722 11087.536069 -1646.743231
## 632 633 634 635 636
## 33198.536353 -6800.877788 5544.523418 5215.305232 -2461.046691
## 637 638 639 640 641
## -5515.330549 -2078.319559 -12554.116057 -2311.760669 -1946.888666
## 642 643 644 645 646
## -2574.505974 -2903.999586 1777.646745 4383.170577 16897.712518
## 647 648 649 650 651
## 18420.138271 684.847669 4599.655468 10416.048909 19927.819375
## 652 653 654 655 656
## 466.391013 -28318.540765 -1468.558397 -2403.620587 1772.941758
## 657 658 659 660 661
## -3287.061363 -10698.452234 1630.181532 4185.589580 -1062.290650
## 662 663 664 665 666
## 12981.657466 1265.483816 1719.363980 -11785.949993 1329.266188
## 667 668 669 670 671
## 1133.911451 -5221.667570 -7446.208992 2055.675948 -3731.712074
## 672 673 674 675 676
## 2663.891067 -3400.085949 -9348.725572 -8292.578419 -2946.800012
## 677 678 679 680 681
## 202.556143 2867.001383 716.695813 -3830.820190 -1805.860033
## 682 683 684 685 686
## -1315.761412 -8241.512355 4668.551845 -2243.978598 -1398.231088
## 687 688 689 690 691
## 586.423078 10845.891493 9806.165491 10552.530872 -9758.800986
## 692 693 694 695 696
## -3612.412442 -3184.099766 5837.416220 -10435.043721 -7928.743089
## 697 698 699 700 701
## -8607.701384 -6251.581830 -4706.630453 3118.839331 -4382.320352
## 702 703 704 705 706
## -1873.757650 4244.330024 31111.196221 9466.147176 23386.531253
## 707 708 709 710 711
## 1604.298068 8260.448323 22861.546993 6490.726277 -18262.768327
## 712 713 714 715 716
## 4800.842650 -5462.037738 -106.287551 478.279955 -17265.342382
## 717 718 719 720 721
## -5241.374588 3363.357601 -2989.295912 -12951.033537 4320.872421
## 722 723 724 725 726
## -5522.209437 781.064707 -3899.082372 -12409.101961 1417.398478
## 727 728 729 730 731
## -1822.448504 -9733.879601 17320.494154 1803.627617 -2697.304188
## 732 733 734 735 736
## 5743.167626 -8609.737391 -692.832182 8168.562204 -15331.385861
## 737 738 739 740 741
## -5873.641257 7449.974320 -4753.678725 195.669633 1860.550076
## 742 743 744 745 746
## -1926.621929 -5137.722937 6447.484741 -6249.034793 22731.012407
## 747 748 749 750 751
## 7833.559272 -1946.656401 -7282.749896 23432.129557 -4298.256238
## 752 753 754 755
## 1398.609718 -14417.889481 56131.088884 26893.014988
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17245.99 20096.74 24356.06 24073.67 26429.99 23759.29 24476.51 19702.64
## 10 11 12 13 14 15 16 17
## 19438.87 16778.30 17556.74 14282.04 14333.27 14998.75 16697.46 15015.37
## 18 19 20 21 22 23 24 25
## 16051.34 15424.09 22515.41 21598.27 21077.48 22969.85 22294.99 22948.36
## 26 27 28 29 30 31 32 33
## 24796.63 18717.41 20445.73 28292.87 28350.60 28023.11 25649.54 27053.74
## 34 35 36 37 38 39 40 41
## 30901.59 31250.45 32661.40 30168.88 34142.92 37355.03 34407.44 31214.33
## 42 43 44 45 46 47 48 49
## 30060.66 20628.39 28156.54 30596.07 31686.64 38533.31 38027.26 42695.29
## 50 51 52 53 54 55 56 57
## 46936.86 39625.63 34187.28 29203.75 22339.59 28637.47 25213.83 21506.79
## 58 59 60 61 62 63 64 65
## 25920.67 27181.80 27479.18 27891.52 23757.28 40369.34 42215.61 37455.61
## 66 67 68 69 70 71 72 73
## 41667.82 46589.63 57271.15 55282.44 40507.16 38009.89 41031.57 35324.58
## 74 75 76 77 78 79 80 81
## 30771.13 21462.93 24668.42 20589.03 22677.70 17566.85 19584.80 18810.36
## 82 83 84 85 86 87 88 89
## 17836.87 15903.38 17182.75 20795.32 25218.57 26209.68 26234.67 26852.18
## 90 91 92 93 94 95 96 97
## 30982.42 29811.51 30812.11 28874.13 28072.95 28441.55 28848.64 22418.06
## 98 99 100 101 102 103 104 105
## 25436.86 18458.62 17313.32 15351.60 15651.73 16329.96 20808.19 19890.56
## 106 107 108 109 110 111 112 113
## 23406.04 23195.75 24873.96 27754.32 25249.31 21688.35 21964.11 24613.69
## 114 115 116 117 118 119 120 121
## 35491.76 33682.35 35522.13 38524.19 40482.54 38168.08 32982.63 29319.45
## 122 123 124 125 126 127 128 129
## 31404.88 29682.69 30865.72 38474.96 38116.98 37177.35 34036.96 35820.38
## 130 131 132 133 134 135 136 137
## 41225.00 40664.59 31855.33 33121.70 36315.44 32721.61 31106.87 30190.73
## 138 139 140 141 142 143 144 145
## 26743.47 28159.66 27929.00 25612.42 27639.37 26262.41 19856.57 22890.87
## 146 147 148 149 150 151 152 153
## 20714.86 23695.68 24236.34 25828.71 26011.01 27667.23 28969.59 32005.52
## 154 155 156 157 158 159 160 161
## 27470.06 26732.10 24284.32 30185.74 41598.01 39926.17 37287.84 42314.35
## 162 163 164 165 166 167 168 169
## 43724.67 47144.81 42605.11 37906.47 43338.44 59660.87 61875.74 60287.87
## 170 171 172 173 174 175 176 177
## 57109.63 55506.88 58212.81 57235.70 49518.25 52345.26 56092.53 56128.76
## 178 179 180 181 182 183 184 185
## 63266.44 53756.95 50503.86 41301.12 32829.97 36342.58 46458.59 45913.93
## 186 187 188 189 190 191 192 193
## 51874.07 57626.17 68422.53 73711.94 67398.81 67592.48 74671.56 70342.64
## 194 195 196 197 198 199 200 201
## 65859.92 55090.42 49113.16 50539.22 46128.47 38287.57 44720.27 42945.47
## 202 203 204 205 206 207 208 209
## 42582.99 43061.93 49864.36 58764.60 58393.74 60120.68 61778.53 65574.40
## 210 211 212 213 214 215 216 217
## 75052.66 67125.67 55300.09 49902.22 40887.41 37841.37 40958.88 30966.71
## 218 219 220 221 222 223 224 225
## 47961.24 55291.09 56194.11 78987.73 86492.01 88497.72 96098.46 87038.17
## 226 227 228 229 230 231 232 233
## 81029.17 80610.58 77256.41 76416.87 81159.74 82525.70 76954.28 72161.30
## 234 235 236 237 238 239 240 241
## 77821.94 64453.22 56479.87 48420.56 40021.76 44218.85 46374.24 39816.84
## 242 243 244 245 246 247 248 249
## 33488.59 43783.77 38023.55 41964.11 34218.42 32922.69 36582.49 39409.87
## 250 251 252 253 254 255 256 257
## 30228.00 36173.40 39979.52 45180.72 47902.39 47394.34 57708.01 75226.84
## 258 259 260 261 262 263 264 265
## 75041.66 68344.06 69827.72 66043.84 67491.77 61241.74 50503.11 46525.56
## 266 267 268 269 270 271 272 273
## 46735.23 42837.14 51652.16 47920.63 52072.49 50186.74 54259.13 54555.51
## 274 275 276 277 278 279 280 281
## 60580.38 58211.69 67896.61 61813.89 62024.18 60371.21 66121.19 59843.92
## 282 283 284 285 286 287 288 289
## 56403.53 45942.91 44337.67 61591.23 67128.16 67538.13 64952.52 64038.76
## 290 291 292 293 294 295 296 297
## 68044.68 71959.75 52932.78 43022.57 37026.19 47360.35 50606.20 49719.05
## 298 299 300 301 302 303 304 305
## 73945.34 79898.12 80566.60 85184.78 83381.11 78405.21 81876.33 56744.02
## 306 307 308 309 310 311 312 313
## 53000.50 52679.88 46462.33 43668.31 47276.14 39819.09 38539.59 33094.37
## 314 315 316 317 318 319 320 321
## 36883.62 36063.95 39900.18 37882.10 63700.52 61539.00 63164.61 71172.69
## 322 323 324 325 326 327 328 329
## 73563.00 99076.41 97452.05 73252.12 72048.47 70403.74 62345.43 59464.07
## 330 331 332 333 334 335 336 337
## 29374.96 33067.13 33507.21 35830.04 35170.05 40944.27 42021.21 37262.85
## 338 339 340 341 342 343 344 345
## 36476.28 36603.11 31916.21 37922.71 38586.95 38845.72 39722.62 41510.79
## 346 347 348 349 350 351 352 353
## 43335.07 43086.42 36022.21 26578.20 31929.58 30789.16 30378.69 27992.14
## 354 355 356 357 358 359 360 361
## 32676.91 36435.92 40905.16 39091.87 40353.75 42494.90 49899.48 50450.76
## 362 363 364 365 366 367 368 369
## 50652.68 53118.95 50599.20 50043.04 42678.95 39872.19 36044.33 33820.21
## 370 371 372 373 374 375 376 377
## 29873.02 37170.13 39459.98 47355.48 41319.49 40765.96 39301.42 38833.60
## 378 379 380 381 382 383 384 385
## 29689.71 34293.64 27336.52 35566.27 45912.43 49482.39 47753.57 49747.79
## 386 387 388 389 390 391 392 393
## 55975.82 65473.32 58676.01 53137.20 52865.55 60254.51 60778.55 69458.24
## 394 395 396 397 398 399 400 401
## 58550.06 60118.94 59678.48 59161.69 57646.66 56406.65 43148.39 51745.00
## 402 403 404 405 406 407 408 409
## 50744.14 49709.05 56118.52 48651.47 47961.74 46286.28 41957.99 40787.20
## 410 411 412 413 414 415 416 417
## 38848.59 32935.18 40818.07 43748.16 38413.64 33495.59 48386.32 52236.05
## 418 419 420 421 422 423 424 425
## 56170.37 48629.85 44948.43 43622.71 47213.79 35618.00 35346.85 29598.25
## 426 427 428 429 430 431 432 433
## 35195.29 43533.62 50434.64 47195.92 44260.83 41182.05 41070.09 37543.36
## 434 435 436 437 438 439 440 441
## 33676.76 30907.23 32485.36 34337.13 32339.17 37211.62 43425.16 40173.32
## 442 443 444 445 446 447 448 449
## 39878.94 42889.25 40772.58 44767.25 40008.01 31028.69 29864.65 41236.28
## 450 451 452 453 454 455 456 457
## 40917.40 46575.91 42206.39 42556.50 44179.11 47902.14 37763.27 42618.85
## 458 459 460 461 462 463 464 465
## 38036.17 45615.04 49139.65 51763.61 48488.91 50832.73 51034.53 52783.84
## 466 467 468 469 470 471 472 473
## 52280.78 55228.37 52553.18 57610.93 50862.09 48469.92 47054.23 43674.19
## 474 475 476 477 478 479 480 481
## 47435.20 54908.25 49327.95 51033.40 45815.47 44192.36 47028.99 36429.43
## 482 483 484 485 486 487 488 489
## 29982.60 31854.36 34554.54 36046.45 37013.57 30646.09 43192.63 49861.49
## 490 491 492 493 494 495 496 497
## 56700.66 51405.26 56244.59 63888.20 67704.56 53943.00 44508.61 42531.01
## 498 499 500 501 502 503 504 505
## 42854.64 43646.82 38126.38 40528.43 45836.83 51525.83 52236.43 52347.13
## 506 507 508 509 510 511 512 513
## 46040.88 47382.60 43636.95 46395.99 46061.24 39768.86 40901.41 40075.48
## 514 515 516 517 518 519 520 521
## 41182.55 43826.00 36656.84 31930.08 55850.75 64022.02 67679.70 61050.19
## 522 523 524 525 526 527 528 529
## 62391.41 75994.66 82980.40 57898.19 52761.59 49451.65 53836.29 53341.99
## 530 531 532 533 534 535 536 537
## 43512.88 48511.15 61188.01 55701.81 59103.84 63096.76 60145.55 55169.89
## 538 539 540 541 542 543 544 545
## 48623.73 47277.89 55225.25 54975.34 47525.79 49752.28 49578.73 50273.45
## 546 547 548 549 550 551 552 553
## 40910.40 32735.64 37083.04 45210.08 45006.78 46714.86 40716.98 49741.54
## 554 555 556 557 558 559 560 561
## 50898.33 40664.38 50217.60 58090.49 57454.61 61056.94 56818.74 68594.33
## 562 563 564 565 566 567 568 569
## 85304.35 75382.65 63931.55 68356.43 66478.42 67666.58 59225.79 43194.38
## 570 571 572 573 574 575 576 577
## 50211.98 56124.64 57308.92 59387.10 60034.97 57157.73 69420.01 58779.84
## 578 579 580 581 582 583 584 585
## 52494.82 60106.07 61611.97 54697.20 60971.72 56542.22 53583.02 67170.40
## 586 587 588 589 590 591 592 593
## 52580.42 59934.01 59025.24 52739.48 52043.75 52320.14 43024.19 45824.59
## 594 595 596 597 598 599 600 601
## 40444.21 44695.41 53469.81 46792.58 52659.01 55039.82 60716.07 56869.35
## 602 603 604 605 606 607 608 609
## 61689.19 53247.15 55129.53 55906.85 58217.94 58792.71 58332.89 52502.95
## 610 611 612 613 614 615 616 617
## 59573.53 57631.17 54724.69 51426.62 44380.66 55905.48 59797.81 50721.78
## 618 619 620 621 622 623 624 625
## 61136.91 65327.13 58808.70 81064.28 66168.29 58487.96 60493.41 55840.88
## 626 627 628 629 630 631 632 633
## 46164.08 56884.96 37416.79 37276.84 46857.18 57353.03 55395.18 84160.31
## 634 635 636 637 638 639 640 641
## 74334.19 76537.69 78177.05 72896.76 65606.89 62236.97 50126.76 48493.03
## 642 643 644 645 646 647 648 649
## 47383.22 45863.57 44246.21 46926.40 51549.57 66539.15 80981.44 78101.20
## 650 651 652 653 654 655 656 657
## 79006.09 84884.89 98346.32 93098.40 63331.42 60780.05 57730.63 58716.49
## 658 659 660 661 662 663 664 665
## 55153.02 45553.82 47941.12 52264.29 51455.49 63031.66 62909.21 63199.09
## 666 667 668 669 670 671 672 673
## 51640.16 53001.37 54021.10 49354.07 43326.32 46365.00 43960.82 47451.94
## 674 675 676 677 678 679 680 681
## 45201.58 38030.29 32681.66 32679.16 35431.57 40169.45 42432.68 40434.72
## 682 683 684 685 686 687 688 689
## 40458.33 40907.66 35243.02 41580.26 41077.09 41376.72 43374.68 54095.69
## 690 691 692 693 694 695 696 697
## 62563.47 70622.66 59906.27 55909.10 52787.58 57948.04 48228.89 41920.13
## 698 699 700 701 702 703 704 705
## 35808.30 32523.34 31001.45 36514.89 34776.33 35449.81 41390.09 70085.00
## 706 707 708 709 710 711 712 713
## 76251.18 93819.99 90134.69 92733.17 107776.85 106616.05 83950.01 84297.75
## 714 715 716 717 718 719 720 721
## 75625.43 72724.58 70698.63 53407.09 48799.79 52296.15 49797.89 38899.70
## 722 723 724 725 726 727 728 729
## 44474.50 40741.22 42989.08 40861.67 31557.60 35513.16 36139.17 29766.93
## 730 731 732 733 734 735 736 737
## 47856.66 50107.02 48138.55 53799.31 46196.69 46471.58 54462.67 40897.78
## 738 739 740 741 742 743 744 745
## 37305.45 45816.96 42587.62 44092.02 46864.05 45976.15 42390.94 49388.18
## 746 747 748 749 750 751 752 753
## 44403.27 65390.73 70717.37 66822.04 58747.73 78550.40 71616.39 70534.32
## 754 755
## 55753.91 104532.13
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.815
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 6.538628 0.4849411 3.1016
## t2* 2105.980566 26.2179083 272.9040
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 2.631962 6.667404 12.67513
## 2 lag_depvar 1708.879799 2118.099877 2597.11387Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Aug 26 00:45:18 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Aug 26 00:45:26 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Aug 26 00:45:33 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Aug 26 00:45:41 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Aug 26 00:45:48 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Aug 26 00:45:56 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Aug 26 00:46:04 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Aug 26 00:46:11 2024
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## =-=-=-=-= Iteration 16000 Mon Aug 26 00:46:19 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Aug 26 00:46:26 2024
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_24 %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))| Item | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|
| Agua | 4.664286 | 5.195333 | 5.410333 | 5.849167 | 6.238589 |
| Comida | 337.878571 | 366.009167 | 312.386750 | 317.896583 | 344.346179 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | 52.977143 | 38.104750 | 47.072333 | 29.523000 | 42.750089 |
| Enceres | 39.268286 | 18.259750 | 24.219750 | 14.801167 | 25.542679 |
| Farmacia | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 8.908268 |
| Gas/Bencina | 60.190000 | 42.636000 | 45.575000 | 13.583667 | 34.374393 |
| Diosi | 43.438429 | 55.804250 | 31.180667 | 52.687833 | 43.314250 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| VTR | 21.991429 | 12.829167 | 25.156667 | 19.086917 | 19.467286 |
| Netflix | 2.385286 | 8.713833 | 7.151583 | 7.028750 | 6.971321 |
| Otros | 52.000000 | 5.481667 | 5.000000 | 0.000000 | 16.871071 |
| Total | 614.793429 | 563.738000 | 505.988083 | 474.453167 | 548.784125 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 47 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2444, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2024-09-09 00:04:58 sería de: 38.557 pesos// Percentil 95% más alto proyectado: 41.724,22
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 37873.02 | 37871.98 |
| Lo.80 | 37919.40 | 37932.49 |
| Point.Forecast | 38557.45 | 40369.97 |
| Hi.80 | 40345.99 | 45170.92 |
| Hi.95 | 41325.73 | 47712.38 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(0,1,1)
##
## Coefficients:
## ma1
## -0.8110
## s.e. 0.0908
##
## sigma^2 = 30421: log likelihood = -427.76
## AIC=859.51 AICc=859.71 BIC=863.86paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,0) errors
##
## Coefficients:
## intercept xreg
## 594.1288 13.7354
## s.e. 191.3911 5.9602
##
## sigma^2 = 29498: log likelihood = -432.27
## AIC=870.55 AICc=870.93 BIC=877.12forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 825.1301 | 724.0723 | 784.4050 |
| Lo.80 | 941.6475 | 842.3990 | 874.5554 |
| Point.Forecast | 1161.7540 | 1065.9233 | 1074.0604 |
| Hi.80 | 1381.8605 | 1329.7501 | 1319.0361 |
| Hi.95 | 1498.3778 | 1469.4116 | 1470.5699 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.9
## [7] tidytext_0.4.1 DT_0.32 janitor_2.2.0
## [10] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [13] xts_0.13.2 forecast_8.21.1 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-11
## [19] NLP_0.2-1 tsibble_1.1.4 lubridate_1.9.3
## [22] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.2
## [25] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [28] gsynth_1.2.1 lattice_0.20-45 GGally_2.2.1
## [31] ggplot2_3.5.0 gridExtra_2.3 plotrix_3.8-4
## [34] sparklyr_1.8.4 httr_1.4.7 readxl_1.4.3
## [37] zoo_1.8-12 stringr_1.5.1 stringi_1.8.3
## [40] data.table_1.15.0 reshape2_1.4.4 fUnitRoots_4021.80
## [43] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] uuid_1.2-0 systemfonts_1.0.5 selectr_0.4-2
## [4] lazyeval_0.2.2 websocket_1.4.1 crosstalk_1.2.1
## [7] listenv_0.9.1 digest_0.6.34 foreach_1.5.2
## [10] htmltools_0.5.7 fansi_1.0.6 ggfortify_0.4.16
## [13] magrittr_2.0.3 doParallel_1.0.17 tzdb_0.4.0
## [16] globals_0.16.2 vroom_1.6.5 sandwich_3.1-0
## [19] askpass_1.2.0 timechange_0.3.0 anytime_0.3.9
## [22] tseries_0.10-55 colorspace_2.1-0 xfun_0.42
## [25] crayon_1.5.2 jsonlite_1.8.8 iterators_1.0.14
## [28] glue_1.7.0 gtable_0.3.4 car_3.1-2
## [31] quantmod_0.4.26 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.2 rngtools_1.5.2 Rcpp_1.0.12
## [37] lfe_2.9-0 viridisLite_0.4.2 xtable_1.8-4
## [40] bit_4.0.5 Formula_1.2-5 htmlwidgets_1.6.4
## [43] timeSeries_4032.109 gplots_3.1.3.1 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.8 dbplyr_2.4.0
## [52] chromote_0.2.0 utf8_1.2.4 labeling_0.4.3
## [55] tidyselect_1.2.0 rlang_1.1.3 later_1.3.2
## [58] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [61] cachem_1.0.8 cli_3.6.2 generics_0.1.3
## [64] evaluate_0.23 fastmap_1.1.1 yaml_2.3.8
## [67] processx_3.8.3 knitr_1.45 bit64_4.0.5
## [70] caTools_1.18.2 future_1.33.1 nlme_3.1-153
## [73] doRNG_1.8.6 slam_0.1-50 xml2_1.3.6
## [76] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.15.0
## [79] curl_5.2.0 bslib_0.6.1 highr_0.10
## [82] ps_1.7.6 fBasics_4032.96 Matrix_1.6-5
## [85] its.analysis_1.6.0 urca_1.3-3 vctrs_0.6.5
## [88] pillar_1.9.0 lifecycle_1.0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 bitops_1.0-7 R6_2.5.1
## [94] promises_1.2.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [97] parallelly_1.37.0 codetools_0.2-18 ggstats_0.5.1
## [100] assertthat_0.2.1 boot_1.3-28 gtools_3.9.5
## [103] MASS_7.3-54 openssl_2.1.1 withr_3.0.0
## [106] fracdiff_1.5-3 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4032.109 rmarkdown_2.25
## [112] snakecase_0.11.1 carData_3.0-5 TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))